The accelerated processes of urbanization and industrialization globally have resulted in an increased risk to aquatic environments, posing a significant threat to the sustainable management of water resources and the health of ecosystems. Accurate prediction of water environmental risks is crucial for the prompt identification of potential pollution sources, the safeguarding of water resources, the maintenance of ecological balance, and the support of environmental policy formulation. This study introduces an innovative prediction methodology that integrates the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) with the multivariate data analysis strengths of Random Forest (RF), aiming to enhance the accuracy and applicability of water environmental risk predictions. The results demonstrate that the proposed prediction method enhances the coefficient of determination (R2) performance by 5.8 %, reduces the Mean Absolute Error (MAE) by 21.5 %, decreases the Mean Bias Error (MBE) by 41.5 %, and lowers the Root Mean Square Error (RMSE) by 56.82 %. Furthermore, this study incorporates surface water data from Henan Province for practical application, merging the prediction results with satellite imagery to facilitate intuitive visualization of water environmental risks, thereby enhancing decision-makers' comprehension and response capabilities regarding complex environmental data. This research not only presents a novel methodology for predicting water environmental risks but also elucidates the evolving trends in such risks.

中文翻译：

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基于卷积神经网络-随机森林的水环境风险预测方法


全球城市化和工业化进程的加速导致水生环境风险增加，对水资源的可持续管理和生态系统的健康构成重大威胁。准确预测水环境风险对于迅速识别潜在污染源、保护水资源、维护生态平衡以及支持环境政策制定至关重要。本研究引入了一种创新的预测方法，该方法将卷积神经网络 （CNN） 的空间特征提取能力与随机森林 （RF） 的多元数据分析优势相结合，旨在提高水环境风险预测的准确性和适用性。结果表明，所提出的预测方法将决定系数 （R2） 性能提高了 5.8 %，平均绝对误差 （MAE） 降低了 21.5 %，平均偏差误差 （MBE） 降低了 41.5 %，均方根误差 （RMSE） 降低了 56.82 %。此外，本研究结合河南省地表水数据进行实际应用，将预测结果与卫星图像相结合，促进水环境风险的直观可视化，从而增强决策者对复杂环境数据的理解和响应能力。这项研究不仅提出了一种预测水环境风险的新方法，还阐明了此类风险的演变趋势。